フロンティア探索中の意味地図構築に向けた効率的なプレート発見
Efficient Placard Discovery for Semantic Mapping During Frontier Exploration
YOLOv2とセグメンテーションでドアプレートを検出・位置推定し、探索を中断して確認するアルゴリズムにより、人手なしで部屋の意味情報を地図に付与する手法を提案した。
著者: David Balaban, Harshavardhan Jagannathan, Henry Liu, Justin Hart
分類: cs.RO, cs.AI
原文アブストラクト
Semantic mapping is the task of providing a robot with a map of its environment beyond the open, navigable space of traditional Simultaneous Localization and Mapping (SLAM) algorithms by attaching semantics to locations. The system presented in this work reads door placards to annotate the locations of offices. Whereas prior work on this system developed hand-crafted detectors, this system leverages YOLOv2 for detection and a segmentation network for segmentation. Placards are localized by computing their pose from a homography computed from a segmented quadrilateral outline. This work also introduces an Interruptable Frontier Exploration algorithm, enabling the robot to explore its environment to construct its SLAM map while pausing to inspect placards observed during this process. This allows the robot to autonomously discover room placards without human intervention while speeding up significantly over previous autonomous exploration methods.
